We present a telerobotics research platform that provides complete access to all levels of control via open-source electronics and software. The electronics employs an FPGA to enable a centralized computation and distributed I/O architecture in which all control computations are implemented in a familiar development environment (Linux PC) and low-latency I/O is performed over an IEEE-1394a (FireWire) bus at speeds up to 400 Mbits/sec. The mechanical components are obtained from retired first-generation da Vinci ® Surgical Systems. This system is currently installed at 11 research institutions, with additional installations underway, thereby creating a research community around a common open-source hardware and software platform.

Augmented reality (AR) and robotic-assisted surgery (RAS) are both rapidly evolving technologies in recent years. RAS systems, such as the da Vinci® Surgical System, aim to improve surgical precision and dexterity, as well as access to minimally-invasive procedures, while AR provides an advanced interface to enhance user perception. Combining the features of both, AR-integrated RAS has become an appealing concept with increased interest among the academic community. In this paper, we review the existing literature about AR-integrated RAS. We discuss the hardware components, application paradigms and clinical relevance of the literature. The concept of AR-integrated RAS has been shown to be feasible for various procedures. Encouraging preliminary results include reduced sight diversion and improved situation awareness. Special techniques, e.g., activation-on-demand, are taken into consideration to address visual clutter of the AR interface and ensure that the system is fail-safe. Although AR-integrated RAS is not yet mature, we believe that if the current trend of development continues, it will soon demonstrate its clinical value.

Intraoperative patient registration may significantly affect the outcome of image-guided surgery (IGS). Image-based registration approaches have several advantages over the currently dominant point-based direct contact methods and are used in some industry solutions in image-guided radiation therapy with fixed X-ray gantries. However, technical challenges including geometric calibration and computational cost have precluded their use with mobile C-arms for IGS. We propose a 2D/3D registration framework for intraoperative patient registration using a conventional mobile X-ray imager combining fiducial-based C-arm tracking and graphics processing unit (GPU)-acceleration. The two-stage framework 1) acquires X-ray images and estimates relative pose between the images using a custom-made in-image fiducial, and 2) estimates the patient pose using intensity-based 2D/3D registration. Experimental validations using a publicly available gold standard dataset, a plastic bone phantom and cadaveric specimens have been conducted. The mean target registration error (mTRE) was 0.34±0.04 mm (success rate: 100%, registration time: 14.2 s) for the phantom with two images 90° apart, and 0.99±0.41 mm (81%, 16.3 s) for the cadaveric specimen with images 58.5° apart. The experimental results showed the feasibility of the proposed registration framework as a practical alternative for IGS routines.